Background: Lumbar spinal stenosis (LSS) is a prevalent and disabling cause of low back and leg pain in elderly people and nerve root sedimentation sign (NRSS) has been demonstrated to have high sensitivity and specificity in diagnosing LSS in selected patients. The purpose of this study was to investigate the diagnosis of LSS and the predictive value of NRSS.
Methods: The clinical and imaging data of 176 patients diagnosed with LSS and 156 patients with non-specific low back pain (LBP) were analyzed retrospectively. Transverse magnetic resonance images (MRI) of the narrowest spinal canal in all patients were acquired and graded by two experienced doctors using the Braz classification, Schizas classification and Chen Jia classification. Receiver operating curve (ROC) was used to compare the diagnostic efficacy of the three classifications. Univariate and multivariate logistic regression models were established to predict the surgical indications of LSS patients.
Result: The diagnostic efficacy of Schizas classification (AUC:0.943; 95%CI:0.918,0.969) and Chen Jia classification (AUC:0.942; 95%CI:0.918,0.966) was significantly higher than that of Braz classification (AUC:0.853; 95%CI:0.808,0.898). Chen Jia classification had the highest correlation with the degree of dural sac cross-sectional area (DCSA) stenosis. In the multivariate analysis of LSS surgical indications, Chen Jia classification (odds ratio [OR], 2.127; 95%CI:1.596,2.835), DCSA (OR,0.398; 95%CI:0.169,0.802) and intermittent claudication (OR,9.481; 95%CI:3.439,26.142) were associated with surgical indications.
Conclusion: Among the three types, it is found that Chen Jia classification has better diagnostic efficacy in differentiating LSS from LBP. In addition, Chen Jia classification is simple to be implemented in clinical practice and has high clinical application value. Hence, Chen Jia classification can be used as an effective surgical treatment indicator for LSS patients.
Keywords: Diagnosis; Lumbar spinal stenosis; Magnetic resonance imaging; Nerve root sedimentation sign.
© 2023. The Author(s).